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  1. Change detection via geospatial graphs.

    Abstract not provided.
  2. Trajectory analysis via a geometric feature space approach.

    Abstract not provided.
  3. LDRD final report :

    Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report amore » preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.« less
  4. Geospatialtemporal remote sensing analysis using semantic graphs.

    Abstract not provided.
  5. Image-Based Algorithms - Semantic Graph Algorithms.

    Abstract not provided.
  6. A Computational Framework for Ontologically Storing and Analyzing Very Large Overhead Image Sets.

    Abstract not provided.
  7. A Computational Framework for Ontologically Storing and Analyzing Very Large Overhead Image Sets.

    Abstract not provided.
  8. Temporal Analysis and Change Detection via Geospatial-Temporal Semantic Graphs.

    Abstract not provided.
  9. Facility Search in Remote Sensing Data Using Geospatial Semantic Graphs.

    Abstract not provided.
  10. Approximate Pattern Matching Under Uncertainty in Geospatial Semantic Graphs.

    Abstract not provided.
...

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"Brost, Randolph C."

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